{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d80fb56",
   "metadata": {},
   "source": [
    "# Propagation Evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e55f2e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "from PIL import Image\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "\n",
    "# 防止内核挂掉\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d494491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c6af1164",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Diffractive_Layer(torch.nn.Module):\n",
    "    # 模型初始化（构造实例），默认实参波长为532e-9，网格总数50，网格大小20e-6，z方向传播0.002。\n",
    "    def __init__(self, λ = 532e-9, N_pixels = 28*5, pixel_size = 20e-6, distance = 0.01):\n",
    "        super(Diffractive_Layer, self).__init__() # 初始化父类\n",
    "        \n",
    "        # 以1/d为单位频率，得到一系列频率分量[0, 1, 2, ···, N_pixels/2-1,-N_pixels/2, ···, -1]/(N_pixels*d)。\n",
    "        fx = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fy = np.fft.fftshift(np.fft.fftfreq(N_pixels, d = pixel_size))\n",
    "        fxx, fyy = np.meshgrid(fx, fy) # 拉网格，每个网格坐标点为空间频率各分量。\n",
    "\n",
    "        argument = (2 * np.pi)**2 * ((1. / λ) ** 2 - fxx ** 2 - fyy ** 2)\n",
    "\n",
    "        # 计算传播场或倏逝场的模式kz，传播场kz为实数，倏逝场kz为复数\n",
    "        tmp = np.sqrt(np.abs(argument))\n",
    "        kz = torch.tensor(np.where(argument >= 0, tmp, 1j*tmp))\n",
    "        self.phase = torch.exp(1j * kz * distance).to(device)\n",
    "\n",
    "    def forward(self, E):\n",
    "        # 定义单个衍射层内的前向传播\n",
    "        E = E.to(device)\n",
    "        fft_c = torch.fft.fft2(E) # 对电场E进行二维傅里叶变换\n",
    "        c = torch.fft.fftshift(fft_c) # 将零频移至张量中心\n",
    "        angular_spectrum = torch.fft.ifft2(torch.fft.ifftshift(c * self.phase)) # 卷积后逆变换得到响应的角谱\n",
    "        return angular_spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aac6134e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def propagation_along_z(initial_field, wl, N_pixels, pixel_size, z_step, number_of_z_steps):\n",
    "    diffraction_step = Diffractive_Layer(distance = z_step, λ = wl, N_pixels = N_pixels, pixel_size = pixel_size)\n",
    "    z_ind = np.arange(0, number_of_z_steps)\n",
    "    full_cross_section =  torch.zeros((len(z_ind),N_pixels, N_pixels),dtype = torch.complex64)\n",
    "    full_cross_section[0] = torch.tensor(initial_field)\n",
    "    with torch.no_grad():\n",
    "        for ind in z_ind[1:]:\n",
    "            full_cross_section[ind] = diffraction_step(full_cross_section[ind-1])\n",
    "            \n",
    "        return full_cross_section\n",
    "\n",
    "DISCRETIZATION_STEP = 1.0e-6 # 网格大小\n",
    "N_pixels = 640 # xy平面边长的元素点数量\n",
    "z_step = 1e-3 # z方向步长\n",
    "number_of_z_steps = 5 # z方向步数\n",
    "z = np.arange(0, number_of_z_steps)*z_step # z坐标\n",
    "wl = 532e-9 # 波长\n",
    "coord_limit = (N_pixels//2)*DISCRETIZATION_STEP # 建立xy坐标系\n",
    "# 拉网格\n",
    "mesh = np.arange(-coord_limit, coord_limit, DISCRETIZATION_STEP)\n",
    "x, y = np.meshgrid(mesh, mesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "30328f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 高斯光束传输演化\n",
    "BEAM_SIZE = 10e-6\n",
    "beam = np.exp(-(x**2+y**2)/BEAM_SIZE**2)\n",
    "field = propagation_along_z(beam, wl, N_pixels, DISCRETIZATION_STEP, z_step, number_of_z_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5e520b57",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 6)\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, constrained_layout=True)\n",
    "\n",
    "# input\n",
    "XY_field_in = np.abs(field[0].data.numpy())\n",
    "ax1.pcolormesh(mesh*10**6, mesh*10**6, XY_field_in)\n",
    "ax1.set_xlabel('x (mm)')\n",
    "ax1.set_ylabel('y ($\\mu$m)')\n",
    "\n",
    "# yx cross-section\n",
    "YZ_field = torch.abs(field[:, N_pixels//2])\n",
    "ax2.pcolormesh(z*10**3, mesh*10**6, YZ_field.T)\n",
    "ax2.set_xlabel('z (mm)')\n",
    "ax2.set_ylabel('y ($\\mu$m)')\n",
    "\n",
    "# output\n",
    "XY_field_out = torch.abs(field[-1]).T\n",
    "ax3.pcolormesh(mesh*10**6, mesh*10**6, XY_field_out.T)\n",
    "ax3.set_xlabel('x (mm)')\n",
    "ax3.set_ylabel('y ($\\mu$m)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "36749316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([157.0796, 157.0796, 157.0796, 157.0795, 157.0795])\n"
     ]
    }
   ],
   "source": [
    "full_int = (torch.abs(field)**2).sum(dim=(1,2)) # 统计总光强\n",
    "print(full_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ac401537",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 200\n",
    "IMG_SIZE = 40\n",
    "N_pixels = 140\n",
    "PADDING = (N_pixels - IMG_SIZE) // 2  # 避免边缘信息丢失\n",
    "\n",
    "# 数据预处理并加载\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Resize((IMG_SIZE, IMG_SIZE))])\n",
    "train_dataset = torchvision.datasets.MNIST(\"./data\", train=True, transform=transform, download=True)\n",
    "val_dataset = torchvision.datasets.MNIST(\"./data\", train=False, transform=transform, download=True)\n",
    "train_dataloader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_dataloader = DataLoader(dataset=val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# 定义一个绘图函数\n",
    "def image_plot(image_E, label):\n",
    "    norm = np.absolute(image_E.cpu().numpy())\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.imshow(np.round(norm, 5)) # 显示图片每个像素点的振幅\n",
    "    ax.axis('off')\n",
    "    ax.set_title(\"E_norm & label={}\".format(label))\n",
    "#     fig.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8dac0227",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [100/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [200/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_number [300/300]\n",
      "classes of the first batch: [0 1 2 3 4 5 6 7 8 9], number of classes: 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (4, 4)\n",
    "for i, (images, labels) in enumerate(train_dataloader):\n",
    "    images = images.to(device)\n",
    "    images_padded = F.pad(torch.squeeze(images), pad=(PADDING, PADDING, PADDING, PADDING))\n",
    "    images_E = torch.sqrt(images_padded)\n",
    "    labels = labels.to(device)\n",
    "    if (i + 1) % 100 == 0:\n",
    "        classes = torch.unique(labels).cpu().numpy()\n",
    "        classes_num = len(classes)\n",
    "        print('batch_number [{}/{}]'.format(i + 1, len(train_dataloader)))\n",
    "        print('classes of the first batch: {}, number of classes: {}'.format(classes, classes_num))#  第一个batch的总类\n",
    "        image_plot(images_E[0], labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5b66906e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_19936\\520924168.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  full_cross_section[0] = torch.tensor(initial_field)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_step = 1e-3 # z方向步长\n",
    "number_of_z_steps = 5 # z方向步数\n",
    "\n",
    "coord_limit = (N_pixels//2)*DISCRETIZATION_STEP # 建立xy坐标系\n",
    "# 拉网格\n",
    "mesh = np.arange(-coord_limit, coord_limit, DISCRETIZATION_STEP)\n",
    "\n",
    "images_input = torch.flip(images_E[0], [0])\n",
    "field = propagation_along_z(images_input, wl, N_pixels, DISCRETIZATION_STEP, z_step, number_of_z_steps)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 6)\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, constrained_layout=True)\n",
    "\n",
    "# input\n",
    "XY_field_in = np.abs(field[0].data.numpy())\n",
    "ax1.pcolormesh(mesh*10**6, mesh*10**6, XY_field_in)\n",
    "ax1.set_xlabel('x (mm)')\n",
    "ax1.set_ylabel('y ($\\mu$m)')\n",
    "\n",
    "# yx cross-section\n",
    "YZ_field = torch.abs(field[:, :, N_pixels//2])\n",
    "ax2.pcolormesh(z*10**3, mesh*10**6, YZ_field.T)\n",
    "ax2.set_xlabel('z (mm)')\n",
    "ax2.set_ylabel('y ($\\mu$m)')\n",
    "\n",
    "# output\n",
    "XY_field_out = torch.abs(field[-1]).T\n",
    "ax3.pcolormesh(mesh*10**6, mesh*10**6, XY_field_out.T)\n",
    "ax3.set_xlabel('x (mm)')\n",
    "ax3.set_ylabel('y ($\\mu$m)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "451d9953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([328.9740, 328.9741, 328.9741, 328.9742, 328.9742])\n"
     ]
    }
   ],
   "source": [
    "full_int = (torch.abs(field)**2).sum(dim=(1,2)) # 统计总光强\n",
    "print(full_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff7172b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "D2NN",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
